Szczegóły publikacji
Opis bibliograficzny
Acquiring and predicting multidimensional diffusion (MUDI) data : an open challenge / Marco Pizzolato, Marco Palombo, Elisenda Bonet-Carne, Chantal M. W. Tax, Francesco Grussu, Andrada Ianus, Fabian BOGUSZ, Tomasz PIĘCIAK, Lipeng Ning, Hugo Larochelle, Maxime Descoteaux, Maxime Chamberland, Stefano B. Blumberg, Thomy Mertzanidou, Daniel C. Alexander, Maryam Afzali, Santiago Aja-Fernández, Derek K. Jones, Carl-Fredrik Westin, Yogesh Rathi, Steven H. Baete, Lucilio Cordero-Grande, Thilo Ladner, Paddy J. Slator, Joseph V. Hajnal, Jean-Philippe Thiran, Anthony N. Price, Farshid Sepehrband, Fan Zhang, Jana Hutter // W: Computational Diffusion MRI : MICCAI workshop, Shenzhen, China, October 2019 / eds. Elisenda Bonet-Carne, [et al.]. — Cham : Springer Nature Switzerland AG, cop. 2020. — (Mathematics and Visualization ; ISSN 1612-3786). — ISBN: 978-3-030-52892-8; e-ISBN: 978-3-030-52893-5. — S. 195-208. — Bibliogr., Abstr. — Publikacja dostępna online od: 2020-11-07. — T. Pięciak - dod. afiliacja: Laboratorio de Procesado de Imagen (LPI), Universidad de Valladolid, Spain
Autorzy (30)
- Pizzolato Marco
- Palombo Marco
- Bonet-Carne Elisenda
- Tax Chantal M. W.
- Grussu Francesco
- Ianus Andrada
- AGHBogusz Fabian
- AGHPięciak Tomasz
- Ning Lipeng
- Larochelle H.
- Descoteaux Maxime
- Chamberland Maxime
- Blumberg Stefano B.
- Mertzanidou Thomy
- Alexander Daniel C.
- Afzali Maryam
- Aja-Fernández Santiago
- Jones Derek K.
- Westin Carl-Fredrik
- Rathi Yogesh
- Baete Steven H.
- Cordero-Grande Lucilio
- Ladner Thilo
- Slator Paddy J.
- Hajnal Joseph V.
- Thiran Jean-Philippe
- Price Anthony N.
- Sepehrband Farshid
- Zhang Fan
- Hutter Jana
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 131038 |
|---|---|
| Data dodania do BaDAP | 2020-11-18 |
| DOI | 10.1007/978-3-030-52893-5_17 |
| Rok publikacji | 2020 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | Medical Image Computing and Computer Assisted Intervention |
| Czasopismo/seria | Mathematics and Visualization |
Abstract
In magnetic resonance imaging (MRI), the image contrast is the result of the subtle interaction between the physicochemical properties of the imaged living tissue and the parameters used for image acquisition. By varying parameters such as the echo time (TE) and the inversion time (TI), it is possible to collect images that capture different expressions of this sophisticated interaction. Sensitization to diffusion-summarized by the b-value-constitutes yet another explorable “dimension” to modify the image contrast, which reflects the degree of dispersion of water in various directions within the tissue microstructure. The full exploration of this multidimensional acquisition parameter space offers the promise of a more comprehensive description of the living tissue but at the expense of lengthy MRI acquisitions, often unfeasible in clinical practice. The harnessing of multidimensional information passes through the use of intelligent sampling strategies for reducing the amount of images to acquire, and the design of methods for exploiting the redundancy in such information. This chapter reports the results of the MUDI challenge, comparing different strategies for predicting the acquired densely sampled multidimensional data from sub-sampled versions of it.